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Exploring the Core Principles, Architectures, and Advancements of Large Language Models
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Author(s): Suriya Murugan (Sri Eshwar College of Engineering, India)and Anandakumar Haldorai (Sri Eshwar College of Engineering, India)
Copyright: 2026
Pages: 20
Source title:
Theory, Practice, and Future Direction of Large Language Models
Source Author(s)/Editor(s): Ismail Lamaakal (University Mohammed Premier, Morocco), Yassine Maleh (Sultan Moulay Slimane University, Morocco), Khalid El Makkaoui (University Mohammed Premier, Morocco), Ibrahim Ouahbi (University Mohammed Premier, Morocco)and Ahmed Abd El-Latif (Prince Sultan University, Saudi Arabia)
DOI: 10.4018/979-8-3693-8387-2.ch003
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Abstract
Language is necessary for human interaction because it helps in communication, learning, and the creation of rules and agreements that advance society. Although computers have historically had difficulty understanding and producing human language, advances in artificial intelligence, especially with regard to large language models (LLMs), have significantly enhanced computers' ability to grasp and produce natural language. LLMs, which are based on deep learning architectures such as transformers, have revolutionized natural language processing (NLP) by facilitating complex text generation and comprehension. Even with their successes, LLMs have drawbacks, like as theoretical issues and possible political and cultural biases in the training set. This chapter explores the underlying theories and architectural frameworks of LLMs, as well as the principles that guide their operation. The impact of these potent models on how humans engage with information and technology is highlighted, and it also covers current study on the difficulties and moral issues surrounding their use.
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